






import argparse

import torch

from transformers import GPTaLMHeadModel, RobertaForMaskedLM


if __name__ == "__main__":
 snake_case__   :						Union[str, Any]													=   argparse.ArgumentParser(
     description=(
         """Extraction some layers of the full RobertaForMaskedLM or GPT2LMHeadModel for Transfer Learned"""
         """ Distillation"""
     )
 )
 parser.add_argument("""--model_type""", default="""roberta""", choices=["""roberta""", """gpt2"""])
 parser.add_argument("""--model_name""", default="""roberta-large""", type=str)
 parser.add_argument("""--dump_checkpoint""", default="""serialization_dir/tf_roberta_048131723.pth""", type=str)
 parser.add_argument("""--vocab_transform""", action="""store_true""")
 snake_case__   :						Dict													=   parser.parse_args()

 if args.model_type == "roberta":
  snake_case__   :						Any													=   RobertaForMaskedLM.from_pretrained(args.model_name)
  snake_case__   :						Dict													=   """roberta"""
 elif args.model_type == "gpt2":
  snake_case__   :						Optional[int]													=   GPTaLMHeadModel.from_pretrained(args.model_name)
  snake_case__   :						List[str]													=   """transformer"""

 snake_case__   :						List[Any]													=   model.state_dict()
 snake_case__   :						Optional[int]													=   {}

 # Embeddings #
 if args.model_type == "gpt2":
  for param_name in ["wte.weight", "wpe.weight"]:
   snake_case__   :						int													=   state_dict[f'{prefix}.{param_name}']
 else:
  for w in ["word_embeddings", "position_embeddings", "token_type_embeddings"]:
   snake_case__   :						Optional[Any]													=   f'{prefix}.embeddings.{w}.weight'
   snake_case__   :						Optional[int]													=   state_dict[param_name]
  for w in ["weight", "bias"]:
   snake_case__   :						Optional[int]													=   f'{prefix}.embeddings.LayerNorm.{w}'
   snake_case__   :						List[str]													=   state_dict[param_name]

    # Transformer Blocks #
 snake_case__   :						Optional[Any]													=   0
 for teacher_idx in [0, 2, 4, 7, 9, 1_1]:
  if args.model_type == "gpt2":
   for layer in ["ln_1", "attn.c_attn", "attn.c_proj", "ln_2", "mlp.c_fc", "mlp.c_proj"]:
    for w in ["weight", "bias"]:
     snake_case__   :						Union[str, Any]													=   state_dict[
         f'{prefix}.h.{teacher_idx}.{layer}.{w}'
     ]
   snake_case__   :						Optional[Any]													=   state_dict[f'{prefix}.h.{teacher_idx}.attn.bias']
  else:
   for layer in [
       "attention.self.query",
       "attention.self.key",
       "attention.self.value",
       "attention.output.dense",
       "attention.output.LayerNorm",
       "intermediate.dense",
       "output.dense",
       "output.LayerNorm",
   ]:
    for w in ["weight", "bias"]:
     snake_case__   :						int													=   state_dict[
         f'{prefix}.encoder.layer.{teacher_idx}.{layer}.{w}'
     ]
  std_idx += 1

 # Language Modeling Head ###s
 if args.model_type == "roberta":
  for layer in ["lm_head.decoder.weight", "lm_head.bias"]:
   snake_case__   :						Optional[int]													=   state_dict[f'{layer}']
  if args.vocab_transform:
   for w in ["weight", "bias"]:
    snake_case__   :						List[str]													=   state_dict[f'lm_head.dense.{w}']
    snake_case__   :						Dict													=   state_dict[f'lm_head.layer_norm.{w}']
 elif args.model_type == "gpt2":
  for w in ["weight", "bias"]:
   snake_case__   :						Optional[int]													=   state_dict[f'{prefix}.ln_f.{w}']
  snake_case__   :						str													=   state_dict["""lm_head.weight"""]

 print(f'N layers selected for distillation: {std_idx}')
 print(f'Number of params transferred for distillation: {len(compressed_sd.keys())}')

 print(f'Save transferred checkpoint to {args.dump_checkpoint}.')
 torch.save(compressed_sd, args.dump_checkpoint)










from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


a      :  Optional[Any]			     =			{
    'configuration_mvp': ['MVP_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MvpConfig', 'MvpOnnxConfig'],
    'tokenization_mvp': ['MvpTokenizer'],
}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a      :  str			     =			['MvpTokenizerFast']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a      :  Optional[Any]			     =			[
	    'MVP_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'MvpForCausalLM',
	    'MvpForConditionalGeneration',
	    'MvpForQuestionAnswering',
	    'MvpForSequenceClassification',
	    'MvpModel',
	    'MvpPreTrainedModel',
	]

if TYPE_CHECKING:
	from .configuration_mvp import MVP_PRETRAINED_CONFIG_ARCHIVE_MAP, MvpConfig, MvpOnnxConfig
	from .tokenization_mvp import MvpTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_mvp_fast import MvpTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_mvp import (
		    MVP_PRETRAINED_MODEL_ARCHIVE_LIST,
		    MvpForCausalLM,
		    MvpForConditionalGeneration,
		    MvpForQuestionAnswering,
		    MvpForSequenceClassification,
		    MvpModel,
		    MvpPreTrainedModel,
		)

else:
	import sys

	a      :  Optional[Any]			     =			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






